8 citations · 16 across the 8 of their papers we have counts for
4 papers · 1 filter
Robust Transferable Feature Extractors: Learning to Defend Pre-Trained Networks Against White Box Adversaries
Alexander Cann, Ian Colbert, Ihab Amer
The widespread adoption of deep neural networks in computer vision applications has brought forth a significant interest in adversarial robustness. Existing research has shown that…
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations
Xinyu Zhang, Ian Colbert, Ken Kreutz-Delgado +1
Quantization and pruning are core techniques used to reduce the inference costs of deep neural networks. State-of-the-art quantization techniques are currently applied to both the…
Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework
Siqiao Ruan, Ian Colbert, Ken Kreutz-Delgado +1
The use of Deep Learning hardware algorithms for embedded applications is characterized by challenges such as constraints on device power consumption, availability of labeled data,…
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems
Alexander Potapov, Ian Colbert, Ken Kreutz-Delgado +2
Stochastic-sampling-based Generative Neural Networks, such as Restricted Boltzmann Machines and Generative Adversarial Networks, are now used for applications such as denoising, im…